玉米植株三维点云茎叶分割与表型解析

3D Point Cloud Stem-Leaf Segmentation and Phenotypic Analysis of Maize Plants

摘要:

植物表型分析在精细农业、作物育种及生产管理中具有重要意义，其中玉米表型研究对于提升产量与品质、推动农业现代化发展具有重要作用。三维点云技术因其高精度与丰富的结构信息，逐渐成为植物表型研究的重要手段。相比传统二维图像方法，三维点云能够更准确地描述作物器官的空间形态，为植株生长监测与表型特征提取提供了新的技术支撑。然而，现有点云分割方法在处理玉米茎叶时仍存在挑战，尤其是在顶部新叶识别、相互重叠叶片分割以及茎秆与叶领边界划分等方面，影响了表型参数测量的精度。为此，提出了一种基于距离场的玉米点云茎叶分割方法。在茎秆提取环节引入Quickshift++算法与闵可夫斯基距离场，结合带约束因子的中值归一化生长分割策略，实现了茎秆的精确提取；同时改进了基于点云骨架与最优传输距离的分割框架，以提升茎叶边界识别的准确性。利用自建点云数据集和公开数据集进行了实验验证。结果表明，该方法能够有效提升茎叶分割的准确性，并显著提高茎高、茎径、叶长及叶宽等表型特征的提取精度。研究成果可为玉米表型学研究提供技术支撑，并为农业智能化与作物精准管理提供参考。

Abstract:

Plant phenotyping plays a vital role in precision agriculture, crop breeding, and production management, among which maize phenotyping research is of particular significance for yield improvement, quality enhancement, and agricultural modernization. With the advantages of high precision and rich structural information, 3D point cloud technology has emerged as an important tool in plant phenotyping. Compared with traditional 2D image-based methods, point clouds provide a more accurate description of plant organ morphology, thereby enabling precise monitoring of maize growth and extraction of phenotypic traits. Nevertheless, existing point cloud segmentation methods still face challenges in maize stem-leaf analysis, especially in recognizing newly emerging leaves, segmenting overlapping or closely spaced leaves, and delineating stem-leaf boundaries, which restricted the accuracy of phenotypic parameter measurement. To address these issues, a distance field-based stem-leaf segmentation method for maize point clouds was proposed. Specifically, Quickshift++ and Minkowski distance fields were integrated with a constrained median-normalized region growing algorithm for precise stem extraction. Furthermore, the segmentation framework based on skeleton and optimal transport distance has been refined, enhancing the accuracy of boundary recognition between stems and leaves. Experiments were conducted on both self-collected and public maize point cloud datasets. The results demonstrated that the proposed method significantly improved segmentation accuracy and enhanced the precision of phenotypic trait extraction, including stem height, stem diameter, leaf length, and leaf width. The research result can provide methodological support for maize phenotyping and offer valuable references for intelligent agriculture and precision crop management.